资 源 简 介
MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences
BACKGROUND:
MicroRNAs are a family of ~22nt small RNAs that can regulate gene expression at the post-transcriptional level. Identification of these molecules and their targets can aid understanding of regulatory processes. Recently, high throughput sequencing (HTS) has become a common identification method but there are two major limitations associated with the technique. Firstly, the method has low efficiency, with typically less than 1 in 10,000 sequences representing miRNA reads and secondly the method preferentially targets highly expressed miRNAs. If sequences are available, computational methods can provide a screening step to investigate the value of an HTS study and aid interpretation of results. However, current methods can only predict miRNAs for short fragments and have usually been trained against small datasets which don"t always reflect t
